Author: Jonathan Nacionales

I recently had a conversation with an architect about the wisdom contained inside Fred Brook’s “The Mythical Man-Month”. We both agreed that it contains timeless principles and practices.

At some point, we discussed how his “surgical team” concept might apply to modern development, and we both concluded that we should structure teams around the “surgical team” concept.

I want to tell this story because it clearly demonstrates how we can apply timeless principles to any “modern” problems.

In “The Mythical Man-Month”, Fred Brooks makes two observations::

Communication overhead is the single largest problem in software development: the more developers on the project, the bigger the communication overhead.

There is a huge productivity gap among software developers: the good developers are very very good. The bad ones are very very bad.

Given these limitations, Dr. Brooks says that we should organize large software development projects into multiple “surgical teams”.

Each “surgical team” has a single “chief programmer” (i.e. the surgeon) who does most of the delicate work.

Much like a surgeon, the “chief programmer” has staff of specialists with more mundane roles to support him.

Contrast this to the current fad of “full-stack” development.

In this model, we build a teams that can handle everything from end-to-end and that can deliver features independently of each other. This is effectively treating people like interchangeable “man-months”, which Dr. Brooks debunks as a “myth”.

I do not want to berate “full-stack” developers, though. They have their place in the world (probably as support for an architect or chief programmer)..

However, a strategy of only hiring full-stack developers does seem very efficient to me because it does not address the two biggest problems that the “Mythical Man Month” addresses.

TL;DR: To create a product backlog, “vertically slice” your user stories by grouping similar scenarios. Estimate the work and value of these slices instead of the user story.

I’ve seen a rise in the demand for “full-stack” developers in the last couple years.

The agile concept of “vertical slicing” made these types of positions very popular.

In a traditional team structure, each person on a team will have knowledge of one layer of an application. When the team attempts to complete some feature, they will have to split the feature into tasks corresponding to layers and then distribute the task to the proper people.

We call this “horizontal slicing”.

If you had a team of “full-stack” developers then you could simply assign a feature to a developer and you could expect them to complete the feature end-to-end with little to no help or coordination.

We call this “vertical slicing”.

This works great in theory, but it has a lot of challenges in practice.

One of these challenges is the exact means of creating high quality product backlog with “vertical slices”.

Enter User Stories

I typically see teams create vertical slices based on “User Stories” and the Gherkin syntax.

The following two code snippets provide examples for two fictitious features: (a) Create Account and (b) Login.

Unfortunately, this sometimes creates very large product backlog items that the team can not deliver quickly.

In particular, I’ve seen this happen when the business dictates that teams can only release polished and bug-free features.

For example, it could take a very long time for a team to complete the scenarios “Internal Server Error” and “Wait Too Long” for the feature “Create Account’. Further, those scenarios may not deliver much business value compared to the necessary work.

In comparison, it could take a very short time for a team to complete the scenario “Valid Account Creation”, and that scenario might have very high business value.

This illustrates that coupling all scenarios together can impede the early and frequent releases we need to create a tight feedback loop between developers and testers or users.

Slice Your Slices

User Stories are not bad, though. We just need a better way to generate vertical slices for our product backlog.

Notice that each user story has multiple scenarios, and that we can conceptually break up each user story into individual scenarios.

Based on this principle, we can create vertical slices by grouping scenarios based on business value.

For example, we could slice our features in the following way.

Feature

Vertical Slice

Scenario

Create Account

Basic

Valid Account Creation

Business Rule Violations

Duplicate Username

Duplicate Email

User Input Errors

Not a Strong Password

Passwords Do Not Match

System Problems

Long Wait Time

Internal Server Error

Login

Basic

Valid Username/Password

Business Rule Violations 1

Invalid Username/Password

Business Rule Violations 2

Too Many Incorrect Attempts

System Problems

Long Wait Time

Long Wait Time

Each of these “vertical slices” become product backlog items that we can individually estimate and prioritize.

For example, our fictitious product team could prioritize the “vertical slices” in the following way.

Create Account – Basic

Login – Basic

Login – Business Rule Violations 1

Create Account – User Input Errors

Create Account – Business Rule Violations

Login – Business Rule Violations 2

Create Account – System Problems

Login – System Problems

This allows a more granular approach to creating product backlog items.

As an added benefit, you can leave a “user story” largely undefined so long as you already have its highest priority slices within your product backlog.

This allows you to “groom” your user stories in a “just in time” way.

For example, we created 4 “vertical slices” of the feature “Create Account” in the example above. However, as an alternative, we could simply create the first slice “Create Account – Basic” and not bother with further analysis until someone completes that slice. This could have saved everyone from spending unnecessary time in a grooming session.

I am only providing an illustration, though. Ultimately, the end result depends on the situation and the interaction between team members.

I recently stumbled upon an idea that I think can measure defects in user experience, and I want to put it down in writing so I have a starting point for further research.

The germ of this idea took root in my mind after my last blog post.

In my last blog post, I applied traditional software engineering principles to developing javascript SPAs, and I used automated testing of user stories as an example.

I also happen to have a project where I use scalacheck to automate generative tests for machine learning algorithms and data pipelining architectures.

Further, I happen to have some experience with six sigma from my days working as a defense contractor.

By combining the different disciplines of (a) user story mapping, (b) generative testing, and (c) six sigma, I believe that we can measure the “defects of user experience” inherent in any “system under test”.

Let’s discuss each discipline in turn.

User Story Mapping

User story mapping is an approach to requirements gathering that uses concrete examples of “real world” scenarios to avoid ambiguity.

Each scenario clearly defines the context of the system and how the system should work in a given case, and ideally, describe something that we can easily test with an automated testing framework.

For example, here is a sample “create account” user story

One of the limitations of testing user stories is that they cannot give you a measure of the correctness of your application. This is because to “prove” program correctness with programatic tests we would need to check every single path through our program.

However, to be fair, the goal of user stories is to gather requirements and provide an “objective” measurement system by which developers, product, and qa can agree to in advance.

Nevertheless, we still need a means of providing some measure of “program correctness”.

Enter Generative Testing.

Generative Tests

Generative testing tests programs using randomly generated data. This enables you to provide a probabilistic measurements of program correctness. However, this assumes that you know how to setup an experimental design that you can use to measure the accuracy of your program.

For example, the scalacheck documentation provides the following snippet of code that tests the java string class.

If you run scalacheck with StringSpecification as input then scalacheck would randomly generate strings and check whether the properties that you defined in StringSpecification are true.

Here is the result that scalacheck would provide if you ran it with StringSpecification as input.

We can see that scalacheck successfully ran 400 tests against StringSpecification.

Let’s do a little statistical analysis to figure out what this means about our the string class.

According to one view of statistics, every phenomenon has a “true” probability distribution which is really just an abstract mathematical formula, and we use data collection methods to estimate the parameters of the “true” distribution.

We will assume this is the case for this discussion.

Suppose that we do not know anything the String class. Under this assumption, the maximum entropy principledictates that we assign a 1 to 1 odds to every test that scalacheck runs.

That basically means that we should treat every individual test like a coin flip. This is known as a Bernoulli trial.

Now, some really smart guy named Wassily Hoeffding figured out a formula that we could use to bound the accuracy and precision of an experiment based exclusively on the number of trials. We, unsurprisingly, call it Hoeffding’s inequality.

I will not bother explaining the math. I’m sure that I’d do a horrible job at it.

It would also be boring.

I will instead give a basic breakdown of how the number of trials relate to the accuracy and precision of your experiment.

Consider the margin of error as a measurement of the experimental results reliability, and the confidence interval as a measurement of the experimental method’s reliability.

For example, if I had an experiment that used 80 trials and I obtained a point estimate of 50% then this would mean that the “real” value is somewhere between 40% and 60% and that the experiment itself would be correct 95 times out of 100.

In other words, 5% of the time an experiment like this one would generate completely bogus numbers.

Now that I have explained that, let us apply this concept to our StringSpecification object. Based on the fact that we had 400 successful runs we can objectively say that the String class’s “true” accuracy is roughly between 95% – 100%, and that there is only a 1% chance that I am completely wrong.

Easy. Right?

I totally understand if you didn’t understand a single thing of what I just said. Are you still reading?

You might be able to set-up an experimental design and measure the results if you are a statistician. However, it is probably beyond the ability of most people.

It would be nice if there was some general set of methods that we could apply in a cookie cutter way, but still have robust results.

Enter Six Sigma.

Six Sigma

Officially, Six Sigma is a set of techniques and tools for process improvement; so, I do not believe that it is generally applicable to software engineering. However, there are a few six sigma techniques that I think are useful.

For example, we could probably use DPMO to estimate how often out system would create a bad user experience (this is analogous to creating a bad part in a manufacturing process).

DPMO stands for Defects per million opportunities, and it is defined by the formula

Let’s suppose that we decided to use scalacheck to test user stories with randomly generated values.

This would immediately open up the prospect of measuring “user experience” using DPMO.

According to the scenario, there are two things that would make this test fail:

not seeing the message “Account Created”

not seeing the link to the login screen

Suppose that we ran 200 randomized tests based on this user story, and had 5 instances where we did not see the message “Account Created” and 2 instances where we did not see the link to the login screen.

This means we have 7 defects out of 2 opportunities from 200 samples. Therefore, DMPO = (7 / (200*2)) = 0.0175 * 1,000,000 = 17,500, which implies that if we left our system in its current state then we can expect to see 17,500 defects for every 1,000,000 created accounts.

Notice how much easier the math is compared to the math for pure generative testing.

These are formulas and techniques that an average person could understand and make decisions from.

That is huge.

Conclusion

This is just me thinking out loud and exploring a crazy idea. However, my preliminary research suggests that this is very applicable. I’ve gone through a few other six sigma tools and techniques and the math seems very easy to apply toward the generative testing of user stories.

I did not provide a concrete example of how you would use scalacheck to generatively test user stories because I didn’t want it to distract from the general concept. In a future blog post, I will use an example scenario to walk through a step-by-step process of how it could work in practice.

Gathering Requirements

We want our “system under test” to be testable; so, we need to place some architectural restrictions to guarantee “testability”.

At a minimum, a testable system must have the qualities of (a) observability, and (b) controllability.

Observability refers to the property that I can arbitrarily observe the states of the “system under test”, and controllability refers to the property that I can put the “system under test” into an arbitrary state.

The two properties are inherently related to each other: you can’t fully observe a “system under test” that you can’t fully control, and there we don’t care to fully control a “system under test” that we can’t fully observe.

I claim (but will not prove) that these architectural restrictions guarantee “testability”.

In order to support the “controllability” requirement of our architecture, we will use the following background block to load fake data into our “system under test” for every new test run.

By design, a user story will have various scenarios, and we will use the scenarios to determine what needs to be “observable”.

For this demonstration we will consider the following scenarios:

Visit the visualization page

Change the selected date on the page

Aggregate the items by month

Aggregate items by year

This snippet shows the scenario description for “visit visualization page”

This snippet shows the scenario description for “change the selected date on the page”:

This snippet shows the scenario description for “aggregate the items by month”:

This snippet shows the scenario description for “aggregate items by year”:

Testing the User Story

The following code snippets uses cucumber.js and babel (an es6 to es5 transpiler) to test the user stories. I use babel because it lets me use generators and coroutines to simplify the asynchronous nature of selenium code.

This snippet initializes the “system under test” based on the data provided in the background block for the user story. This satisfies the “controllability” property of our architecture.

This snippet executes the action(s) that change the state of the “system under test” for the behavior “When I visit the page at <some_url>”:

This snippet executes the action(s) that change the state of the “system under test” for the behavior “When I select the date <some_date>”:

This snippet executes the actions(s) that change the state of the “system under test” for the behavior “When I select to aggregate by <some_aggregation_type>”:

This snippet executes the post conditions checks of the “system under test” for the block “Then I should see a bar chart for <some_title>”.

Note: I will skip the rest of the blocks for brevity’s sake.

In the acceptance tests, I use a page object to encapsulate the data retrieval algorithms for each page.

For example, the following code snippet shows how the page object encapsulates the algorithm that “observes” when the page has finished loading.

This test looks for a DOM element with the html attribute “qa-chart-type”, and then waits for it to change.

We have to do this because the SPA fetches data using a http request when if first loads, and we want to detect when the SPA receives a response.

However, since we cannot easily detect when the SPA receives an http response, we make the SPA communicate this to our test script by creating and updating special “Quality Assurance” html attributes.

Architects typically have nothing but contempt for this architecture due to its well known rigidity, fragility, immobility, and viscosity.

These are well known application level architectural properties:

rigidity measures how easy an architecture can respond to change.

fragility measure how likely something will break when making a change

mobility measures how easy an architecture can support moving code

viscosity measure how easy an architecture supports maintaining the original design

When faced with a monolithic architecture, most architects I know would refactor the monolith into something that looks like this.

src/

controller/

Controller001.java

Controller002.java

view/

View001.jsp

View002.jsp

model/

Model001.java

Model002.java

lib/

use_case001.jar

use_case002.jar

use_case003.jar

Each .jar file is a self contained components that is independently testable, releasable, and versioned.

You might think that simply moving code from a directory to a .jar file does not significantly improve the design. However, when applied properly, it has a huge influence on reducing rigidity, fragility, viscosity, and increasing mobility.

We will use this approach, but in this case we will use npm modules as components: a npm module is equivalent to a JAVA .jar file; so, all the same rules apply.

From the scenarios in the user story, I can identify the following responsibilities:

Bar Chart Visualization

User Interface Interaction

Data Retrieval

QA HTML Attribute Generation

Therefore, I will respectively create the four following packages:

analytics-chart

analytics-facade

analytics-service

qa-locator-utility

Create the “Analytics Chart” Component

The actual details of how I created the component are not important; so, I will not talk about it.

However, I feel that I should mention the unit tests I wrote for the visualizations because it isn’t a common thing.

I created unit tests to prove partial correctness of my application using the approach I describe in my previous blog post.

For example, the following code snippet tests the visualization for some items.

This test verifies that certain DOM elements appear in the svg canvas. I consider it a very weak test because there is a good chance that it could pass and the visualization could still be wrong. However, it is also a cheap test to write; so, there is still value in writing it.

Create the “Analytics Facade” Component

This component encapsulates all the user interface “glue logic” (i.e. identifying which screen or page to display), and we use the command pattern and delegation pattern to promote loose coupling between the user interface and this component.

For example, these are the cucumber features that I wrote for this component.

From a client’s perspective, they only need to know how to execute a command from the facade, and how to delegate tasks to the facade. The cucumber tests above provide a very high level description of what types of commands and delegates this facade exposes.

For the sake of completeness, I will also show you the step definitions for this feature.

Create the “Analytics Service” Component

Create the “QA Locator Utility” Component

Officially, we do not need this component, but we would like to have an easy method to locate elements in our window’s DOM using xpath. This component encapsulates any algorithms that we might need to simplify the process of creating html attributes for the purpose of writing acceptance tests.

I don’t consider it a very interesting component; so, I will not discuss it.

Composing the Components Into A Single Page Application Framework

To demonstrate how to migrate the application between frameworks, I will compose the components into (a) Backbone 1.2, and (b) React 0.14.

The following component diagram shows the general relationships between the components and framework.

Backbone 1.2 and React 0.14

We have placed the bulk of the applications functionality inside of framework independent components. Therefore, in order to create a backbone version or a react version of the application, we have to create a special component that coordinates between our “independent” components and the framework.

In the case of React, we created the component backbone_app.

In the case of Backbone, we create the component react_app.

For example, here is the entry point for our Backbone application:

Here is the entry point for our React application:

The parallels in the code are very apparent. I will not go into detail into the code bases because it is not important. I only care about the “shape” of the design.

You can find the Backbone implementation here, and the React implementation here if you really care to dig into the code, however.

Conclusion

This approach might seem very complicated for such a simple application … and it is.

In reality, this approach only makes sense once your application reaches a certain scale. However, simple applications have a tendency of becoming big applications, and it is very expensive to put a test harness on an already existing system with no tests.

Personally, I recommend you always build an application with decoupled testable components from the very beginning because that investment will pay large dividends in the long run.

Further, decoupling your components from the framework has huge benefits even if you have no intention of switching frameworks.

One practical “use case” is with automated testing.

When you decouple your components from the framework then you can test the bulk of your application independent of the framework. This significantly simplifies the overall testing process.

In fact, the package principles were partially created to minimize the work of creating and maintaining tests.

In a future post, I will show you how you can use a Continuous Integration tool to automatically run your tests whenever someone releases a new version of a component.

For a couple weeks, I have been using cucumber.js and selenium webdriver for acceptance testing.

For the most part, I’ve been very happy with tools. However, the pervasent use of promises in selenium webdriver annoys me.

For example, consider the following script that opens up a login page, and tests that you can successfully login.

I personally don’t like the 2 levels of indirection from using promises.

Further, the number of promises will scale with each page you visit.

This leads to very painful reading.

Just recently, I found a way to increase the understandability of selenium webdriver based scripts using es6 generators.

Since node.js does not support es6 yet, you will have to use the babel-node executable to make it work. You can installing babel-node with the following script from the command line:

The following code does the exact same thing as the previous script, but instead uses es6.

The code uses lazy evaluation to stop and continue execution of the script. If you are familiar with functional languages like scala, clojure, or haskell then this type of approach should be second nature to you.

I will give you a high level overview of what is happening:

Step 1: We define a generator function called main.

Step 2: In main, we explicitly tell it to stop execution at two locations. We signal to the javascript engine to stop execution using the keyword yield.

Step 3: We instantiate main and assign the instance to the variable m. The javascript engine will execute the function and stop execution at the first yield statement.

However, after it stops execution, it will call the function visitLoginPage.

Step 4: visitLoginPage will use the variable m to get a reference to the main function, and either tell it to continue or to throw and exception.

Step 5: The main function will continue executing until it reaches the second yield statement.

The javascript engine will stop execution of the function, and it will call the function getUrl.

Step 6: getUrl will continue execution and return a value that we store in currentUrl in the main function.

Now we can print the url to the console.

Conclusion

I’m sure that there are ways alternative ways to simplify selenium webdriver scripts.

I’m not saying that this is the best way. It is just something that I personally like, and I hope that it helps you.

Semantic version numbers consist of the pattern of x.y.z where x, y, and z are integers.

Each position conveys a particular meaning (hence the name “semantic versioning”).

The first number is the major version.

We usually start a package with version 0. We should considered a version 0 package as unfinished, experimental, heavily changing without too much care for backwards compatibility.

Starting from major version 1, we consider the published API as stabilized and that the package has a certain trustworthiness from that moment. Every next increment of the major version marks the moment that parts of the code breaks backward compatibility.

The second part of the version number is the minor version.

We increment the minor versions when we add new functionality to the package or deprecate parts of the public API. A minor release promises your clients that the package will not break backwards compatibility. A minor version only adds new ways of using the package.

The last part of the version number is the patch version.

Starting with version 0 it is incremented for each patch that is released for the package. This can be either a bug fix, or some refactored private code.

Further, a package maintainer has the option to add meta-data after the release numbers. Typically they will use it to classifying packages as having a particular state: alpha, beta, or rc (release candidate).

For example, these items could be the releases of a package:

2.10.2

3.0.0

3.1.0

3.1.1-alpha

3.1.1-beta

3.1.1-rc.1

3.1.1-rc.2

3.1.1

These number communicate the following to a client:

release 2.10.x has two patches. The patches may have been bug fixes or refactors. We would need to look at the changelog or commit logs to determine the importance of each patch.

After release 2.10.x, the package maintainer decided to break backwards compatibility. The package maintainer signaled to clients that the package breaks backwards compatibility by creating the 3.0.0 release.

At 3.1.0, the package maintainer introduced new features that did not break compatibility with 3.0.0.

3.1.1-alpha signals that the package maintainer started a patch of release 3.1.0. However, the package maintainer does not want to call the patch stable. Someone may have submitted a bug report to the package maintainer for release 3.1.0, and the package maintainer may have started the initial phases of fixing the bug. In this scenario, the package maintainer likely added some testing code to isolate the particular bug, or validate that the bug is fixed.

3.1.1-beta suggests that the package maintainer completed the “feature”. Most likely this signals that the package maintainer’s automated tests pass.

3.1.1-rc.1 suggests that the package passed manual QA and that the package manager can potentially release the patch as a stable version. The package manager would likely tell clients to run their integration tests against this release. Manual QA likely happen against this release, also.

3.1.1-rc.2 suggests that the package maintainer found regression errors in 3.1.1-rc.1. It may indicate that an integration test failed for a client. The package manager may have fixed issues that a client reported and released the fix as 3.1.1-rc.2.

3.1.1 signals that the package maintainer has successfully patched the 3.1.0 release.

The Common Reuse Principle

The Common Reuse Principle states that “code that is used together should be group together”. The reverse is also true: “code that is not used together should not be grouped together.”

A dependency on a package implies a dependency on everything within the package; so, when a package changes, all clients of that package must verify that they work with the new version.

If we package code that we do not use together then we will force our clients to go through the process of upgrading and revalidating their package unnecessarily.

By obeying the Common Reuse Principle, we provide the simple courtesy of not making our clients work harder than necessary.

A good architect will divide a large project into a network of interrelated packages.

Ideally, we would like to minimize the number of packages for every change request because when we minimize the number of effected packages we also minimize the work to manage, test, and release those packages. The more packages that change in any given release, the greater the work to rebuild, test, and deploy the release.

When we obey the Common Closure Principle we force a requirements change to happen to the smallest number of packages possible, and prevent irrelevant releases.

Automated Tests are Mandatory

While clean code and clean design of your package is important, it’s more important that your package behaves well.

In order to verify the proper behavior of a package, you must have automated tests.

There exists many different opinions on the nature and extent of automated tests, though:

How many tests should you write?

Do you write the tests first, and the code later?

Should you add integration tests, or functional tests?

How much code coverage does your package need?

In my opinion, the particulars of how you write tests or the extent of your tests are situational. However, you must have automated tests. This is non-negotiable.

If you don’t have automated tests then you are essentially telling your clients this:

I do not care about you. This works for me today, and I do not care about tomorrow. Use this package at your own parel.

Putting the Principles to Practice

Now that we have the principles, we can start to apply it.

In a future post, I will create a basic application that uses the package design principles that I described. Further, I will compose the components into different frameworks to demonstrate how to migrate the application between frameworks.

I sometimes hate software frameworks. More precisely, I hate their rigidity, and cookie cutter systems of mass production.

Personally, when someone tells me about a cool new feature of a framework, what I really hear is “look at the shine on those handcuffs.”

I’ve defended this position on many occasions. However, I really want to put it down on record so that I can simply point people at this blog post the next time they asks me to explain myself.

Why Use a Framework?

Frameworks are awesome things.

I do not dispute that.

Just from the top of my head, I can list the following reasons why you should use a framework

A framework abstracts low level details.

The community support is invaluable.

The out of the box features enable you not to reinvent the wheel.

A framework usually employs design patterns and “best practices” that enables team development

I’m sure that you can add even better reasons to this list.

So why would I not be such a big fan?

What Goes Wrong with Frameworks?

VENDOR LOCK-IN.

Just to reinforce the point, let me say that again: VENDOR LOCK-IN.

There is a natural power asymmetry between the designer/creators of a framework and the users of that framework.

Typically, the users of the framework are the slaves to the framework, and the framework designers are the masters. It doesn’t matter if it is designed by some open source community or large corporation.

In fact, this power asymmetry is why vendors and consultants can make tons of money on “free” software: you are really paying them for their specialized knowledge.

Once you enter into a vendor lock-in, the vendor, consulting company, or consultant can charge you any amount of money they want. Further, they can continue to increase their prices at astronomical rates, and you will have no choice but to pay it.

Further, the features of your application become more dependent on the framework as the system grows larger. This has the added effect of increasing the switching cost should you ever want to move to another framework.

I’ll use a simple thought experiment to demonstrate how and why this happens.

Define a module as any kind of organizational grouping of code. It could be a function, class, package, or namespace.

Suppose that you have two modules: module A and module B. Suppose that module B has features that module A needs; so, we make module A use module B.

In this case we can say that module A depends on module B, and we can visualize that dependency with the following picture.

Suppose that we introduce another module C, and we make module B use module C. This makes module B depend on module C, and consequently makes module A depend on module C.

Suppose that I created some module D that uses module A. That would make module D depend on module A, module B, and module C.

This demonstrates that dependencies are transitive, and every new module we add to the system will progressively make the transitive dependencies worse.

“What’s so bad about that?” you might ask.

Suppose that we make a modification to module C. We could inadvertently break module D, module A, and module B since they depend on module C.

Now, you might argue that a good test suite would prevent that … and you would be right.

However, consider the amount of work to maintain your test suite.

If you changed module C then you would likely need to add test cases for it, and since dependencies are transitive you would likely need to alter/add test cases for all the dependent modules.

The transitive dependencies make it nearly impossible to reuse your modules.

Suppose you wanted to reuse module A. Well, you would also need to reuse module B and module C since module A depends on them. However, what if module B and module C don’t do anything that you really need or even worse do something that impedes you.

You have forced a situation where you can’t use the valuable parts of the code without also using the worthless parts of the code.

When faced with this situation, it is often easier to create an entirely new module with the same features of module A.

In my experience, this happens more often than not.

There is a module in this chain of dependencies that does not suffer this problem: module C.

Module C does not depend on anything; so, it is very easy to reuse. Since it is so easy to reuse, everyone has the incentive to reuse it. Eventually you will have a system that looks like this.

So it seems that we are at an impasse: we want to reuse infrastructure code from a framework, but we also want our business logic to be independent of it.

Can’t we have it both?

Actually, we can.

The direction of the arrow in our dependency graph makes our code immobile.

For example, if module A depends on module B then we have to use module B every time we use module A.

However, if we inverted the dependencies such that module A and module B both depended on a common module — call it module C — then we could reuse module C since it has no dependencies. This makes module C a reusable component; so, this is where you would place your reusable code.

For example, standard Component Oriented Architecture will break large C++ applications into multiple dll files, large JAVA applications into multiple jar files, or large .NET applications into multiple assemblies.

There exists rules about how you structure these packages, however.

You could argue that we would spend too much time with up front design when designing this type of system.

I agree that there is some up-front cost, but that cost is easily off-set by the time savings on maintenance, running tests, adding features, and anything else that we want to do to the system in the future.

We should view writing mobile code as an investment that will pay big dividends over time.

I plan to create a very simple demo application that showcases this design in the near future. Stay tuned.